In the following, I’ll illustrate how to load the raw data and calculate various metrics
library(tidyverse)
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## ✓ tibble 3.1.3 ✓ dplyr 1.0.7
## ✓ tidyr 1.1.3 ✓ stringr 1.4.0
## ✓ readr 1.4.0 ✓ forcats 0.5.1
## Warning: package 'ggplot2' was built under R version 3.6.2
## Warning: package 'tibble' was built under R version 3.6.2
## Warning: package 'tidyr' was built under R version 3.6.2
## Warning: package 'readr' was built under R version 3.6.2
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## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()
library(lubridate)
## Warning: package 'lubridate' was built under R version 3.6.2
##
## Attaching package: 'lubridate'
## The following objects are masked from 'package:base':
##
## date, intersect, setdiff, union
library(scales)
## Warning: package 'scales' was built under R version 3.6.2
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## Attaching package: 'scales'
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## discard
## The following object is masked from 'package:readr':
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## col_factor
library(RcppRoll)
The following will of course be specific to your environment
data<-read_csv("./output/tweetjson006_annotated_tweets.csv")
## Warning: Missing column names filled in: 'X1' [1]
##
## ── Column specification ────────────────────────────────────────────────────────
## cols(
## .default = col_double(),
## tweet_created_at = col_datetime(format = ""),
## tweet_text = col_character(),
## tweet_entities = col_character(),
## tweet_public_metrics = col_character(),
## tweet_referenced_tweets_id = col_character(),
## tweet_referenced_tweets_type = col_character(),
## date_floor = col_datetime(format = "")
## )
## ℹ Use `spec()` for the full column specifications.
data